> ## Documentation Index > Fetch the complete documentation index at: https://wb-21fd5541-sdk-optional-dependencies.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Python SDK 0.28.0 > Browse the W&B Python SDK API reference including installation instructions, classes, and function documentation. The W\&B Python SDK, accessible at `wandb`, enables you to train and fine-tune models, and manage models from experimentation to production. > After performing your training and fine-tuning operations with this SDK, you can use [the Public API](/models/ref/python/public-api) to query and analyze the data that was logged, and [the Reports and Workspaces API](/models/ref/wandb_workspaces) to generate a web-publishable [report](/models/reports/) summarizing your work. ## Sign up and create an API key To authenticate your machine with W\&B, you must first generate an API key at [User Settings](https://wandb.ai/settings). ## Install and import packages Install the W\&B Python SDK using `pip`: ```shell theme={null} pip install wandb ``` ## Import W\&B Python SDK The following code snippet demonstrates how to import the W\&B Python SDK and initialize a run. Replace `` with your team entity name. ```python theme={null} import wandb # Specify your team entity entity = "" # Project that the run is recorded to project = "my-awesome-project" with wandb.init(entity=entity, project=project) as run: run.log({"accuracy": 0.9, "loss": 0.1}) ``` ## Python SDK extras Install optional Python extras to extend the functionality of the W\&B Python SDK. Specify the name of the extra you want to install within square brackets after `wandb`. The syntax is: ```shell theme={null} pip install wandb[extra] ``` For example, to install W\&B with Google Cloud Storage support, run: ```shell theme={null} pip install wandb[gcp] ``` Install more than one optional dependency by separating them with commas: ```shell theme={null} pip install wandb[gcp,aws,media] ``` The following table lists Python SDK extras and their suggested use cases. | Extra | Packages included | Install if you | | ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------- | | `gcp` | [google-cloud-storage](https://pypi.org/project/google-cloud-storage/) | Use `gs://` artifact references. | | `aws` | [boto3](https://pypi.org/project/boto3/), [botocore](https://pypi.org/project/botocore/) | Use `s3://` artifact references. | | `azure` | [azure-identity](https://pypi.org/project/azure-identity/), [azure-storage-blob](https://pypi.org/project/azure-storage-blob/) | Use Azure Blob Storage artifact references. | | `media` | [numpy](https://pypi.org/project/numpy/), [moviepy](https://pypi.org/project/moviepy/), [imageio](https://pypi.org/project/imageio/), [pillow](https://pypi.org/project/pillow/), [bokeh](https://pypi.org/project/bokeh/), [soundfile](https://pypi.org/project/soundfile/), [plotly](https://pypi.org/project/plotly/), [rdkit](https://pypi.org/project/rdkit/) | Log images, video, audio, or plots from raw data (numpy arrays, tensors). | | `sweeps` | [sweeps](https://pypi.org/project/sweeps/) | Run local sweep controller (`wandb.controller()`). | | `workspaces` | [wandb-workspaces](https://pypi.org/project/wandb-workspaces/) | Programmatically manage workspaces. | The following tabs provide more details about each extra, including installation instructions, dependencies, and code examples. Use the `gcp` extra if you add reference artifacts that start with `gs://` URIs. Installation: ```shell theme={null} pip install wandb[gcp] ``` Dependencies: * `google-cloud-storage` Example: ```python theme={null} import wandb artifact = wandb.Artifact("my-artifact", type="dataset") artifact.add_reference("gs://bucket/path/to/file") with wandb.init() as run: run.log_artifact(artifact) ``` Use the `aws` extra if you add reference artifacts that start with `s3://` URIs. Installation: ```shell theme={null} pip install wandb[aws] ``` Dependencies: * `boto3` * `botocore>=1.5.76` Example: ```python theme={null} import wandb artifact = wandb.Artifact("my-artifact", type="dataset") artifact.add_reference("s3://bucket/path/to/file") with wandb.init() as run: run.log_artifact(artifact) ``` Use the `azure` extra if you add artifact references to Azure Blob Storage (URLs ending in `.blob.core.windows.net`). Installation: ```shell theme={null} pip install wandb[azure] ``` Dependencies: * `azure-identity` * `azure-storage-blob` Example: ```python theme={null} import wandb artifact = wandb.Artifact("my-artifact", type="dataset") artifact.add_reference("https://.blob.core.windows.net/container/blob") with wandb.init() as run: run.log_artifact(artifact) ``` Use the `media` extra if you log images, video, audio, or plots from raw data (numpy arrays, tensors). Installation: ```shell theme={null} pip install wandb[media] ``` Dependencies: * `numpy` * `moviepy>=1.0.0` * `imageio>=2.28.1` * `pillow` * `bokeh` * `soundfile` * `plotly>=5.18.0` * `rdkit` The following table lists the dependencies included in the `media` extra and their use cases: | Dependency | Use Case | | ---------------- | --------------------------------------------------------------------------------------------------- | | pillow | Logging images with `wandb.Image()` with numpy arrays or tensors (wandb/sdk/data\_types/image.py) | | moviepy, imageio | Logging videos with `wandb.Video()` with numpy arrays (wandb/sdk/data\_types/video.py) | | soundfile | Logging audio with `wandb.Audio()` with raw numpy data (wandb/sdk/data\_types/audio.py) | | plotly | Logging interactive charts with `wandb.Plotly()` (wandb/sdk/data\_types/plotly.py) | | bokeh | Logging Bokeh plots with `wandb.Bokeh()` (wandb/sdk/data\_types/bokeh.py) | | rdkit | Logging molecular structures with `wandb.Molecule.from_rdkit()` (wandb/sdk/data\_types/molecule.py) | Use the `sweeps` extra if you run a local sweep controller with `wandb.controller()`. Installation: ```shell theme={null} pip install wandb[sweeps] ``` Dependencies: * `sweeps>=0.2.0` The cloud-based sweep agent (`wandb agent`) does NOT require this extra. Use the `workspaces` extra if you programmatically create or edit W\&B workspaces with `wandb.apis.workspaces`. Installation: ```shell theme={null} pip install wandb[workspaces] ``` Dependencies: * `wandb-workspaces`